Automated gas detection techniques

Abstract

Systems and methods are described for an automatic and adaptive scanning method to efficiently scan for gas plumes using an imaging or LiDAR based gas monitoring system. In an example, the gas monitoring system can be coupled to a laser absorption spectroscopy with LiDAR. In an example, systems and methods for optimizing the utilization of the imaging or LiDAR based gas monitoring system includes planning, commissioning, acquiring data automatically, interpreting the data, or extracting gas emission events from the data, or a combination thereof, to provide a complete lifecycle of a gas leak and a comprehensive understanding of the gas emissions. In another example, systems and methods for detecting the presence of a plume of gas includes using supervised machine learning to train a model to recognize which images contain plumes of gas and estimate corresponding rates of gas leakage based on the images.

What is claimed is: 
     
         1 . A method for monitoring an emission of a gas, comprising:
 receiving, from an imaging device, a gas density image;   detecting the emission of the gas in the gas density image;   determining an emission rate, an emission location, or both, of the gas based on the gas density image;   associating the emission of the gas with equipment; and   causing a user interface to be displayed on a display device, wherein the user interface comprises one or more indications of the emission rate, the emission location, or both, of the gas.   
     
     
         2 . The method of  claim 1 , comprising:
 determining a location of the imaging device; and   installing the imaging device at the location.   
     
     
         3 . The method of  claim 2 , comprising calibrating the imaging device after the imagine device is installed at the location. 
     
     
         4 . The method of  claim 1 , wherein the gas density image is a panorama of an environment. 
     
     
         5 . The method of  claim 1 , comprising transmitting a scan plan to the imaging device for acquiring the gas density image. 
     
     
         6 . The method of  claim 1 , wherein the imaging device automatically acquires the gas density image based on the scan plan. 
     
     
         7 . The method of  claim 1 , comprising determining if a gas leak is occurring based on the gas density image. 
     
     
         8 . The method of  claim 1 , comprising creating an emission event based on the emission rate, the emission location, or both, of the gas. 
     
     
         9 . A system for monitoring an emission of a gas, comprising:
 an imaging device;   a methane gas sensor;   a display device;   a memory storage including a non-transitory, computer-readable medium comprising instructions; and   a hardware-based processor that executes the instructions to carry out stages comprising:   receiving, from the imaging device, a gas density image;   detecting the emission of the gas in the gas density image;   determining an emission rate, an emission location, or both, of the gas based on the gas density image;   associating the emission of the gas with equipment; and   causing a user interface to be displayed on the display device, wherein the user interface comprises one or more indications of the emission rate, the emission location, or both, of the gas.   
     
     
         10 . A method for detecting a plume of gas in a gas density image, comprising:
 generating and training a machine learning model based on (1) raw spectroscopy data associated with the gas, one or more plumes of the gas, or both, (2) images generated based on the raw spectroscopy data associated with the gas, the one or more plumes of the gas, or both, or (3) both;   receiving a gas density image from an imaging device;   analyzing the gas density image using the machine learning model to detect a plume of gas; and   causing a display device to display an indication of the detected plume of gas.   
     
     
         11 . The method of  claim 10 , wherein the gas density image comprises two or more gas density images, and the method comprises:
 for each gas density image of the two or more gas density images, determining that the gas density image is indicative of a plume of gas or the gas density image is indicative of noise.   
     
     
         12 . The method of  claim 10 , comprising determining an emission rate of the gas based on the gas density image. 
     
     
         13 . A system for detecting a plume of gas in a gas density image, comprising:
 an imaging device;   a methane gas sensor;   a display device;   a memory storage including a non-transitory, computer-readable medium comprising instructions; and   a hardware-based processor that executes the instructions to carry out stages comprising:   generating and training a machine learning model based on (1) raw spectroscopy data associated with the gas, one or more plumes of the gas, or both, (2) images generated based on the raw spectroscopy data associated with the gas, the one or more plumes of the gas, or both, or (3) both;   receiving a gas density image from an imaging device;   analyzing the gas density image using the machine learning model to detect a plume of gas; and   causing the display device to display an indication of the detected plume of gas.   
     
     
         14 . The system of  claim 13 , wherein the gas density image comprises two or more gas density images, and for each gas density image of the two or more gas density images, the hardware-based processor executes instructions to determine that the gas density image is indicative of a plume of gas or the gas density image is indicative of noise. 
     
     
         15 . The system of  claim 13 , wherein an emission rate of the gas based on the gas density image is determined.